| 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980 |
- # Set the device with environment, default is cuda:0
- # export SENSEVOICE_DEVICE=cuda:1
- import os, re
- from fastapi import FastAPI, File, Form
- from fastapi.responses import HTMLResponse
- from typing_extensions import Annotated
- from typing import List
- from enum import Enum
- import torchaudio
- from model import SenseVoiceSmall
- from funasr.utils.postprocess_utils import rich_transcription_postprocess
- from io import BytesIO
- class Language(str, Enum):
- auto = "auto"
- zh = "zh"
- en = "en"
- yue = "yue"
- ja = "ja"
- ko = "ko"
- nospeech = "nospeech"
- model_dir = "iic/SenseVoiceSmall"
- m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir, device=os.getenv("SENSEVOICE_DEVICE", "cuda:0"))
- m.eval()
- regex = r"<\|.*\|>"
- app = FastAPI()
- @app.get("/", response_class=HTMLResponse)
- async def root():
- return """
- <!DOCTYPE html>
- <html>
- <head>
- <meta charset=utf-8>
- <title>Api information</title>
- </head>
- <body>
- <a href='./docs'>Documents of API</a>
- </body>
- </html>
- """
- @app.post("/api/v1/asr")
- async def turn_audio_to_text(files: Annotated[List[bytes], File(description="wav or mp3 audios in 16KHz")], keys: Annotated[str, Form(description="name of each audio joined with comma")], lang: Annotated[Language, Form(description="language of audio content")] = "auto"):
- audios = []
- audio_fs = 0
- for file in files:
- file_io = BytesIO(file)
- data_or_path_or_list, audio_fs = torchaudio.load(file_io)
- data_or_path_or_list = data_or_path_or_list.mean(0)
- audios.append(data_or_path_or_list)
- file_io.close()
- if lang == "":
- lang = "auto"
- if keys == "":
- key = ["wav_file_tmp_name"]
- else:
- key = keys.split(",")
- res = m.inference(
- data_in=audios,
- language=lang, # "zh", "en", "yue", "ja", "ko", "nospeech"
- use_itn=False,
- ban_emo_unk=False,
- key=key,
- fs=audio_fs,
- **kwargs,
- )
- if len(res) == 0:
- return {"result": []}
- for it in res[0]:
- it["raw_text"] = it["text"]
- it["clean_text"] = re.sub(regex, "", it["text"], 0, re.MULTILINE)
- it["text"] = rich_transcription_postprocess(it["text"])
- return {"result": res[0]}
|
